Building products that feel native to every user, regardless of language, is the best way to establish a user base across the globe. To do this, a product needs to support a variety of locales. The challenge with supporting multiple locales is the maintenance and generation of localized strings, which are deeply integrated into many facets of a product.
Michelle Casbon explores the machine learning and natural language processing that enables Qordoba to generate high-quality translations in many different languages and describes the techniques Qordoba uses to provide continuous deployment of localized strings, live syncing across platforms (mobile, web, Photoshop, Sketch, Help Desk, etc.), content generation for any locale, and emotional response. Michelle also explores Qordoba’s architecture for handling billions of localized strings in many different languages, using Apache Spark and Apache PredictionIO (incubating) for natural language processing, Kubernetes and Docker for containerized deployment, scaling, and management, Apache Cassandra and MariaDB as a storage layer, and Scala and Akka as an orchestration layer.
Michelle Casbon is director of data science at Qordoba. Michelle’s development experience spans more than a decade across various industries, including media, investment banking, healthcare, retail, and geospatial services. Previously, she was a senior data science engineer at Idibon, where she built tools for generating predictions on textual datasets. She loves working with open source projects and has contributed to Apache Spark and Apache Flume. Her writing has been featured in the AI section of O’Reilly Radar. Michelle holds a master’s degree from the University of Cambridge, focusing on NLP, speech recognition, speech synthesis, and machine translation.
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